FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376116194> ?p ?o ?g. }

• W4376116194 endingPage "1668" @default.
• W4376116194 startingPage "1668" @default.
• W4376116194 abstract "Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification." @default.
• W4376116194 created "2023-05-12" @default.
• W4376116194 creator A5008325652 @default.
• W4376116194 creator A5009590404 @default.
• W4376116194 creator A5021512315 @default.
• W4376116194 creator A5033159446 @default.
• W4376116194 creator A5044651824 @default.
• W4376116194 creator A5053082976 @default.
• W4376116194 creator A5055576757 @default.
• W4376116194 creator A5065344326 @default.
• W4376116194 creator A5066775749 @default.
• W4376116194 date "2023-05-09" @default.
• W4376116194 modified "2023-10-02" @default.
• W4376116194 title "Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction" @default.
• W4376116194 cites W1991453852 @default.
• W4376116194 cites W1993515038 @default.
• W4376116194 cites W2028665260 @default.
• W4376116194 cites W2060206486 @default.
• W4376116194 cites W2096879990 @default.
• W4376116194 cites W2098226047 @default.
• W4376116194 cites W2098860390 @default.
• W4376116194 cites W2102721064 @default.
• W4376116194 cites W2123326681 @default.
• W4376116194 cites W2241781419 @default.
• W4376116194 cites W2548590258 @default.
• W4376116194 cites W2802628545 @default.
• W4376116194 cites W2804619940 @default.
• W4376116194 cites W2900489637 @default.
• W4376116194 cites W2982452748 @default.
• W4376116194 cites W3015910149 @default.
• W4376116194 cites W3085402642 @default.
• W4376116194 cites W3112385165 @default.
• W4376116194 cites W3112701542 @default.
• W4376116194 cites W3117186051 @default.
• W4376116194 cites W3126199490 @default.
• W4376116194 cites W3156290655 @default.
• W4376116194 cites W3163349016 @default.
• W4376116194 cites W3184996131 @default.
• W4376116194 cites W3197603055 @default.
• W4376116194 cites W4220666439 @default.
• W4376116194 cites W4319593944 @default.
• W4376116194 cites W4320300814 @default.
• W4376116194 doi "https://doi.org/10.3390/diagnostics13101668" @default.
• W4376116194 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37238157" @default.
• W4376116194 hasPublicationYear "2023" @default.
• W4376116194 type Work @default.
• W4376116194 citedByCount "2" @default.
• W4376116194 countsByYear W43761161942023 @default.
• W4376116194 crossrefType "journal-article" @default.
• W4376116194 hasAuthorship W4376116194A5008325652 @default.
• W4376116194 hasAuthorship W4376116194A5009590404 @default.
• W4376116194 hasAuthorship W4376116194A5021512315 @default.
• W4376116194 hasAuthorship W4376116194A5033159446 @default.
• W4376116194 hasAuthorship W4376116194A5044651824 @default.
• W4376116194 hasAuthorship W4376116194A5053082976 @default.
• W4376116194 hasAuthorship W4376116194A5055576757 @default.
• W4376116194 hasAuthorship W4376116194A5065344326 @default.
• W4376116194 hasAuthorship W4376116194A5066775749 @default.
• W4376116194 hasBestOaLocation W43761161941 @default.
• W4376116194 hasConcept C105795698 @default.
• W4376116194 hasConcept C108583219 @default.
• W4376116194 hasConcept C124504099 @default.
• W4376116194 hasConcept C126838900 @default.
• W4376116194 hasConcept C153180895 @default.
• W4376116194 hasConcept C154945302 @default.
• W4376116194 hasConcept C163892561 @default.
• W4376116194 hasConcept C22029948 @default.
• W4376116194 hasConcept C2776214188 @default.
• W4376116194 hasConcept C2776511800 @default.
• W4376116194 hasConcept C31972630 @default.
• W4376116194 hasConcept C33923547 @default.
• W4376116194 hasConcept C41008148 @default.
• W4376116194 hasConcept C71924100 @default.
• W4376116194 hasConcept C89600930 @default.
• W4376116194 hasConceptScore W4376116194C105795698 @default.
• W4376116194 hasConceptScore W4376116194C108583219 @default.
• W4376116194 hasConceptScore W4376116194C124504099 @default.
• W4376116194 hasConceptScore W4376116194C126838900 @default.
• W4376116194 hasConceptScore W4376116194C153180895 @default.
• W4376116194 hasConceptScore W4376116194C154945302 @default.
• W4376116194 hasConceptScore W4376116194C163892561 @default.
• W4376116194 hasConceptScore W4376116194C22029948 @default.
• W4376116194 hasConceptScore W4376116194C2776214188 @default.
• W4376116194 hasConceptScore W4376116194C2776511800 @default.
• W4376116194 hasConceptScore W4376116194C31972630 @default.
• W4376116194 hasConceptScore W4376116194C33923547 @default.
• W4376116194 hasConceptScore W4376116194C41008148 @default.
• W4376116194 hasConceptScore W4376116194C71924100 @default.
• W4376116194 hasConceptScore W4376116194C89600930 @default.
• W4376116194 hasIssue "10" @default.
• W4376116194 hasLocation W43761161941 @default.
• W4376116194 hasLocation W43761161942 @default.
• W4376116194 hasLocation W43761161943 @default.
• W4376116194 hasLocation W43761161944 @default.
• W4376116194 hasOpenAccess W4376116194 @default.
• W4376116194 hasPrimaryLocation W43761161941 @default.
• W4376116194 hasRelatedWork W2920218276 @default.
• W4376116194 hasRelatedWork W2960184797 @default.

• next